Vars of interest
corr_par <-
final %>%
dplyr::select(
`dep entropy`= scl1depent,
`dep mean` = scl1dep,
`structure` = f1custructure,
`permisiveness` = f1cupermit,
`SES` = f1RRses,
`EP` = f1mextprb,
`IP`= f1mintprb,
`Vagal flexibility` = s,
`RSA neutral` = i)
corr_par <- as.data.frame(corr_par)
#corr_par <- corr_par[,-1]
corPlot(corr_par, upper = F, diag = F, zlim = c(-0.9, 0.9), stars = T, cex = 1.1, pval=T,
cuts=c(.001,.05), n.legend = 8, scale = F, ylas = 1, xlas = 2, main = "Correlations among variables of interest")

Descriptives
## vars n mean sd median trimmed mad min max
## fid 1 180 6227.83 315.27 6105.50 6166.70 80.80 6001.00 6947.00
## f1csex 2 180 1.47 0.50 1.00 1.47 0.00 1.00 2.00
## f1mintprb 3 173 52.61 10.49 52.00 52.81 11.86 29.00 75.00
## f1mextprb 4 173 53.45 11.61 54.00 53.44 14.83 32.00 80.00
## f1cage 5 180 5.37 1.10 4.79 5.36 1.15 3.18 6.92
## i 6 154 0.00 1.15 0.16 0.05 1.29 -3.71 2.76
## s 7 154 0.00 0.25 -0.01 0.01 0.22 -0.97 0.63
## t1baseline 8 154 6.84 1.11 6.87 6.87 1.05 3.67 9.63
## f1RRses 9 180 -0.01 0.87 -0.05 0.02 0.70 -2.42 1.81
## f1custructure 10 177 2.78 0.62 2.75 2.76 0.62 1.25 4.67
## f1cupermit 11 177 1.70 0.80 1.50 1.56 0.74 1.00 4.50
## f1cuwarm 12 177 1.85 0.59 1.75 1.81 0.62 1.00 3.60
## scl1depent 13 178 0.41 0.26 0.41 0.40 0.36 0.00 0.99
## scl1anxent 14 178 30.77 25.33 27.25 28.89 33.46 0.00 91.39
## scl1hosent 15 178 37.88 22.95 39.55 37.91 21.28 0.00 82.62
## scl1dep 16 178 0.53 0.61 0.31 0.42 0.34 0.00 3.38
## scl1anx 17 178 0.36 0.49 0.20 0.26 0.30 0.00 2.80
## scl1hos 18 178 0.50 0.51 0.33 0.42 0.25 0.00 3.50
## physent 19 176 0.48 0.15 0.47 0.50 0.14 0.00 0.77
## range skew kurtosis se
## fid 946.00 1.69 1.00 23.50
## f1csex 1.00 0.11 -2.00 0.04
## f1mintprb 46.00 -0.10 -0.68 0.80
## f1mextprb 48.00 0.02 -0.95 0.88
## f1cage 3.74 0.13 -1.71 0.08
## i 6.47 -0.38 -0.08 0.09
## s 1.60 -0.65 2.03 0.02
## t1baseline 5.96 -0.27 0.24 0.09
## f1RRses 4.23 -0.38 0.11 0.06
## f1custructure 3.42 0.23 0.09 0.05
## f1cupermit 3.50 1.51 1.87 0.06
## f1cuwarm 2.60 0.57 -0.23 0.04
## scl1depent 0.99 0.05 -0.95 0.02
## scl1anxent 91.39 0.38 -0.85 1.90
## scl1hosent 82.62 -0.11 -0.71 1.72
## scl1dep 3.38 2.02 4.93 0.05
## scl1anx 2.80 2.53 7.71 0.04
## scl1hos 3.50 2.06 7.10 0.04
## physent 0.77 -0.79 0.15 0.01
Convergent validity
## Ignored 2 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## ------------------------------------------------------------------------------------
## rho | 0.63 | [0.54, 0.72] | 100% | [-0.05, 0.05] | 0% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 1.53e+19
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)
## Ignored 2 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## ------------------------------------------------------------------------------------
## rho | 0.62 | [0.53, 0.70] | 100% | [-0.05, 0.05] | 0% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 1.29e+18
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)
## Ignored 2 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## ----------------------------------------------------------------------------------------
## rho | -0.18 | [-0.31, -0.04] | 99.20% | [-0.05, 0.05] | 1.47% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 2.87
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)
## Ignored 2 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## ----------------------------------------------------------------------------------------
## rho | -0.24 | [-0.37, -0.09] | 99.90% | [-0.05, 0.05] | 0% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 30.85
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)
## Ignored 5 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## ---------------------------------------------------------------------------------------
## rho | -0.06 | [-0.21, 0.08] | 79.47% | [-0.05, 0.05] | 39.07% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 0.243
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)
## Ignored 5 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## ---------------------------------------------------------------------------------------
## rho | 0.04 | [-0.11, 0.18] | 69.27% | [-0.05, 0.05] | 46.46% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 0.197
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)
## Ignored 8 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## --------------------------------------------------------------------------------------
## rho | 0.20 | [0.05, 0.33] | 99.67% | [-0.05, 0.05] | 0% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 6.23
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)
## Ignored 8 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## ------------------------------------------------------------------------------------
## rho | 0.23 | [0.10, 0.37] | 100% | [-0.05, 0.05] | 0% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 26.14
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)
## Ignored 5 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## ----------------------------------------------------------------------------------------
## rho | -0.21 | [-0.34, -0.07] | 99.83% | [-0.05, 0.05] | 0% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 11.15
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)
## Ignored 5 rows containing missing observations.
## Summary of Posterior Distribution
##
## Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior
## --------------------------------------------------------------------------------------
## rho | -0.27 | [-0.39, -0.12] | 100% | [-0.05, 0.05] | 0% | Beta (3 +- 3)
## Bayes Factors for Model Comparison
##
## Model BF
## [2] (rho != 0) 118.76
##
## * Against Denominator: [1] (rho = 0)
## * Bayes Factor Type: JZS (BayesFactor)

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 5 rows containing non-finite values (stat_smooth).
## Warning: Removed 5 rows containing missing values (geom_point).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 5 rows containing non-finite values (stat_smooth).
## Warning: Removed 5 rows containing missing values (geom_point).

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 5 rows containing non-finite values (stat_smooth).
## Warning: Removed 5 rows containing missing values (geom_point).

is maternal mood entropy related to children’s Physio ( Reactivity (only at T1))
##Controls
sm <- dat[c(1:12,15)]
sm <-
sm %>%
mutate(f1csex= f1csex-1) %>%
mutate(f1csex = as.factor(f1csex))
contrasts(sm$f1csex) <- c(-.5, .5)
d0_lm <-
na.omit(sm)
mod_0 <- lm(scale(s)~
1, data=d0_lm)
mod_i<-
lm(
scale(s) ~
scale(i),
data = d0_lm
)
anova(mod_0, mod_i)
## Analysis of Variance Table
##
## Model 1: scale(s) ~ 1
## Model 2: scale(s) ~ scale(i)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 140 140.00
## 2 139 111.22 1 28.776 35.963 1.647e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model_parameters(mod_i)
## Parameter | Coefficient | SE | 95% CI | t(139) | p
## ---------------------------------------------------------------------
## (Intercept) | -2.50e-18 | 0.08 | [-0.15, 0.15] | -3.31e-17 | > .999
## i | 0.45 | 0.08 | [ 0.30, 0.60] | 6.00 | < .001
mod_base<-
lm(
scale(s) ~
scale(i) +
scale(t1baseline),
data = d0_lm
)
anova(mod_i, mod_base)
## Analysis of Variance Table
##
## Model 1: scale(s) ~ scale(i)
## Model 2: scale(s) ~ scale(i) + scale(t1baseline)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 139 111.22
## 2 138 111.22 1 0.0039492 0.0049 0.9443
model_parameters(mod_base)
## Parameter | Coefficient | SE | 95% CI | t(138) | p
## ---------------------------------------------------------------------
## (Intercept) | -4.90e-18 | 0.08 | [-0.15, 0.15] | -6.48e-17 | > .999
## i | 0.46 | 0.13 | [ 0.21, 0.71] | 3.58 | < .001
## t1baseline | -9.00e-03 | 0.13 | [-0.26, 0.25] | -0.07 | 0.944
#Not necessary to have baseline & i. Model with i fits better? But people are more familiar with baseline
mod_age <-
lm(
scale(s) ~
scale(i) +
scale(f1cage),
data = d0_lm
)
anova(mod_i, mod_age)
## Analysis of Variance Table
##
## Model 1: scale(s) ~ scale(i)
## Model 2: scale(s) ~ scale(i) + scale(f1cage)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 139 111.22
## 2 138 105.28 1 5.9437 7.7909 0.005996 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model_parameters(mod_age)
## Parameter | Coefficient | SE | 95% CI | t(138) | p
## --------------------------------------------------------------------
## (Intercept) | 1.66e-17 | 0.07 | [-0.15, 0.15] | 2.26e-16 | > .999
## i | 0.46 | 0.07 | [ 0.31, 0.60] | 6.18 | < .001
## f1cage | 0.21 | 0.07 | [ 0.06, 0.35] | 2.79 | 0.006
#Age is a significant covariate
mod_sex <-
lm(
scale(s) ~
scale(i) +
scale(f1cage) +
f1csex,
data = d0_lm
)
anova(mod_age, mod_sex)
## Analysis of Variance Table
##
## Model 1: scale(s) ~ scale(i) + scale(f1cage)
## Model 2: scale(s) ~ scale(i) + scale(f1cage) + f1csex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 138 105.28
## 2 137 101.39 1 3.8884 5.2541 0.02342 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model_parameters(mod_sex)
## Parameter | Coefficient | SE | 95% CI | t(137) | p
## ------------------------------------------------------------------
## (Intercept) | 0.01 | 0.07 | [-0.13, 0.16] | 0.18 | 0.858
## i | 0.47 | 0.07 | [ 0.32, 0.61] | 6.42 | < .001
## f1cage | 0.22 | 0.07 | [ 0.07, 0.36] | 2.99 | 0.003
## f1csex [1] | 0.33 | 0.15 | [ 0.05, 0.62] | 2.29 | 0.023
#Sex is also relevant
mod_ep <-
lm(
scale(s) ~
scale(i) +
scale(f1cage) +
f1csex +
scale(f1mextprb),
data = d0_lm
)
anova(mod_sex, mod_ep)
## Analysis of Variance Table
##
## Model 1: scale(s) ~ scale(i) + scale(f1cage) + f1csex
## Model 2: scale(s) ~ scale(i) + scale(f1cage) + f1csex + scale(f1mextprb)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 137 101.39
## 2 136 100.51 1 0.88543 1.1981 0.2756
model_parameters(mod_ep)
## Parameter | Coefficient | SE | 95% CI | t(136) | p
## ------------------------------------------------------------------
## (Intercept) | 0.01 | 0.07 | [-0.13, 0.16] | 0.17 | 0.864
## i | 0.47 | 0.07 | [ 0.32, 0.61] | 6.39 | < .001
## f1cage | 0.23 | 0.07 | [ 0.09, 0.38] | 3.13 | 0.002
## f1csex [1] | 0.32 | 0.15 | [ 0.03, 0.61] | 2.19 | 0.031
## f1mextprb | -0.08 | 0.07 | [-0.23, 0.07] | -1.09 | 0.276
mod_ip <-
lm(
scale(s) ~
scale(i) +
scale(f1cage) +
f1csex +
scale(f1mintprb),
data = d0_lm
)
anova(mod_sex, mod_ip)
## Analysis of Variance Table
##
## Model 1: scale(s) ~ scale(i) + scale(f1cage) + f1csex
## Model 2: scale(s) ~ scale(i) + scale(f1cage) + f1csex + scale(f1mintprb)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 137 101.39
## 2 136 101.34 1 0.051855 0.0696 0.7923
model_parameters(mod_ip)
## Parameter | Coefficient | SE | 95% CI | t(136) | p
## ------------------------------------------------------------------
## (Intercept) | 0.01 | 0.07 | [-0.13, 0.16] | 0.17 | 0.862
## i | 0.47 | 0.07 | [ 0.32, 0.61] | 6.35 | < .001
## f1cage | 0.22 | 0.07 | [ 0.07, 0.37] | 2.99 | 0.003
## f1csex [1] | 0.33 | 0.15 | [ 0.03, 0.62] | 2.17 | 0.032
## f1mintprb | -0.02 | 0.08 | [-0.17, 0.13] | -0.26 | 0.792
mod_ses <-
lm(
scale(s) ~
scale(i) +
scale(f1cage) +
f1csex +
scale(f1RRses),
data = d0_lm
)
anova(mod_sex, mod_ses)
## Analysis of Variance Table
##
## Model 1: scale(s) ~ scale(i) + scale(f1cage) + f1csex
## Model 2: scale(s) ~ scale(i) + scale(f1cage) + f1csex + scale(f1RRses)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 137 101.39
## 2 136 100.22 1 1.1757 1.5955 0.2087
model_parameters(mod_ses)
## Parameter | Coefficient | SE | 95% CI | t(136) | p
## ------------------------------------------------------------------
## (Intercept) | 0.01 | 0.07 | [-0.13, 0.16] | 0.20 | 0.843
## i | 0.47 | 0.07 | [ 0.32, 0.61] | 6.43 | < .001
## f1cage | 0.22 | 0.07 | [ 0.07, 0.36] | 2.97 | 0.004
## f1csex [1] | 0.37 | 0.15 | [ 0.08, 0.66] | 2.49 | 0.014
## f1RRses | 0.09 | 0.07 | [-0.05, 0.24] | 1.26 | 0.209
#EP is not related to slope but is related to entropy, leave it
#SES is related to entropy, leave it
mod_str <-
lm(
scale(s) ~
scale(i) +
scale(f1cage) +
f1csex +
scale(f1custructure),
data = d0_lm
)
anova(mod_sex, mod_str)
## Analysis of Variance Table
##
## Model 1: scale(s) ~ scale(i) + scale(f1cage) + f1csex
## Model 2: scale(s) ~ scale(i) + scale(f1cage) + f1csex + scale(f1custructure)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 137 101.391
## 2 136 99.887 1 1.5042 2.048 0.1547
model_parameters(mod_str)
## Parameter | Coefficient | SE | 95% CI | t(136) | p
## --------------------------------------------------------------------
## (Intercept) | 0.01 | 0.07 | [-0.13, 0.16] | 0.18 | 0.855
## i | 0.48 | 0.07 | [ 0.33, 0.62] | 6.53 | < .001
## f1cage | 0.25 | 0.08 | [ 0.10, 0.40] | 3.28 | 0.001
## f1csex [1] | 0.34 | 0.15 | [ 0.05, 0.63] | 2.33 | 0.021
## f1custructure | 0.11 | 0.08 | [-0.04, 0.26] | 1.43 | 0.155
mod_per <-
lm(
scale(s) ~
scale(i) +
scale(f1cage) +
f1csex +
scale(f1cupermit),
data = d0_lm
)
anova(mod_sex, mod_per)
## Analysis of Variance Table
##
## Model 1: scale(s) ~ scale(i) + scale(f1cage) + f1csex
## Model 2: scale(s) ~ scale(i) + scale(f1cage) + f1csex + scale(f1cupermit)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 137 101.39
## 2 136 101.14 1 0.25394 0.3415 0.5599
model_parameters(mod_per)
## Parameter | Coefficient | SE | 95% CI | t(136) | p
## ------------------------------------------------------------------
## (Intercept) | 0.01 | 0.07 | [-0.13, 0.16] | 0.18 | 0.857
## i | 0.47 | 0.07 | [ 0.33, 0.62] | 6.43 | < .001
## f1cage | 0.22 | 0.07 | [ 0.07, 0.36] | 2.98 | 0.003
## f1csex [1] | 0.34 | 0.15 | [ 0.05, 0.63] | 2.30 | 0.023
## f1cupermit | -0.04 | 0.07 | [-0.19, 0.10] | -0.58 | 0.560
#Parenting does not improve model fit
#SEM models using structure + permisiveness // MLR - bootstrap
#Main model
model <- '
s ~ f1csexc + f1cagec + i + f1RRses + f1cupermitc + f1custructurec + scl1dep + scl1depent + f1mextprbc
f1cupermitc ~~ f1custructurec + 0*f1mextprbc
scl1dep ~~ scl1depent + f1mextprbc
scl1depent ~~ f1mextprbc
f1csexc ~~ 0*f1cagec + f1RRses + 0*f1mextprbc
f1cagec ~~ 0*f1RRses + f1mextprbc + f1custructure c
f1RRses ~~ scl1dep + scl1depent + f1mextprbc
f1RRses ~ 0*1
i ~ 0*1
f1csexc ~ 0*1
f1cagec ~ 0*1
f1cupermitc ~ 0*1
f1custructurec ~ 0*1
'
fit <- sem(model, data = dat, estimator = "MLR", missing = "FIML.x")
summary(fit, standardized=T, fit.measures = T, rsquare=T, ci = T)
## lavaan 0.6-8 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 33
##
## Number of observations 180
## Number of missing patterns 6
##
## Model Test User Model:
## Standard Robust
## Test Statistic 20.553 20.722
## Degrees of freedom 32 32
## P-value (Chi-square) 0.941 0.938
## Scaling correction factor 0.992
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 330.224 291.585
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.133
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.056 1.064
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.056
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1949.455 -1949.455
## Scaling correction factor 1.245
## for the MLR correction
## Loglikelihood unrestricted model (H1) -1939.179 -1939.179
## Scaling correction factor 1.121
## for the MLR correction
##
## Akaike (AIC) 3964.911 3964.911
## Bayesian (BIC) 4070.278 4070.278
## Sample-size adjusted Bayesian (BIC) 3965.767 3965.767
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.012 0.014
## P-value RMSEA <= 0.05 0.998 0.998
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.013
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.048 0.048
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## s ~
## f1csexc 0.101 0.033 3.108 0.002 0.037 0.165
## f1cagec 0.047 0.018 2.617 0.009 0.012 0.082
## i 0.106 0.019 5.534 0.000 0.068 0.143
## f1RRses 0.044 0.021 2.051 0.040 0.002 0.085
## f1cupermitc -0.017 0.020 -0.814 0.416 -0.056 0.023
## f1custructurec 0.027 0.028 0.971 0.332 -0.027 0.081
## scl1dep 0.090 0.047 1.903 0.057 -0.003 0.182
## scl1depent -0.250 0.111 -2.253 0.024 -0.467 -0.032
## f1mextprbc -0.001 0.002 -0.831 0.406 -0.005 0.002
## Std.lv Std.all
##
## 0.101 0.197
## 0.047 0.201
## 0.106 0.471
## 0.044 0.147
## -0.017 -0.051
## 0.027 0.065
## 0.090 0.213
## -0.250 -0.255
## -0.001 -0.068
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## f1cupermitc ~~
## f1custructurec -0.128 0.037 -3.496 0.000 -0.200 -0.056
## f1mextprbc 0.000 0.000 0.000
## scl1dep ~~
## scl1depent 0.128 0.013 9.664 0.000 0.102 0.154
## f1mextprbc 1.043 0.558 1.871 0.061 -0.050 2.137
## scl1depent ~~
## f1mextprbc 0.683 0.221 3.093 0.002 0.250 1.116
## f1csexc ~~
## f1cagec 0.000 0.000 0.000
## f1RRses -0.064 0.031 -2.079 0.038 -0.124 -0.004
## f1mextprbc 0.000 0.000 0.000
## f1cagec ~~
## f1RRses 0.000 0.000 0.000
## f1mextprbc 1.904 0.911 2.090 0.037 0.118 3.691
## f1custructurec -0.186 0.045 -4.144 0.000 -0.274 -0.098
## f1RRses ~~
## scl1dep 0.098 0.033 2.978 0.003 0.034 0.163
## scl1depent 0.050 0.016 3.013 0.003 0.017 0.082
## f1mextprbc 2.294 0.749 3.061 0.002 0.825 3.762
## Std.lv Std.all
##
## -0.128 -0.259
## 0.000 0.000
##
## 0.128 0.806
## 1.043 0.148
##
## 0.683 0.225
##
## 0.000 0.000
## -0.064 -0.148
## 0.000 0.000
##
## 0.000 0.000
## 1.904 0.150
## -0.186 -0.273
##
## 0.098 0.186
## 0.050 0.219
## 2.294 0.229
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## f1RRses 0.000 0.000 0.000
## i 0.000 0.000 0.000
## f1csexc 0.000 0.000 0.000
## f1cagec 0.000 0.000 0.000
## f1cupermitc 0.000 0.000 0.000
## f1custructurec 0.000 0.000 0.000
## .s 0.053 0.036 1.468 0.142 -0.018 0.125
## scl1dep 0.537 0.045 11.909 0.000 0.449 0.625
## scl1depent 0.409 0.019 21.383 0.000 0.371 0.446
## f1mextprbc 0.112 0.842 0.134 0.894 -1.538 1.763
## Std.lv Std.all
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.053 0.208
## 0.537 0.882
## 0.409 1.562
## 0.112 0.010
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .s 0.044 0.006 7.513 0.000 0.032 0.055
## f1csexc 0.249 0.002 120.561 0.000 0.245 0.253
## f1cagec 1.203 0.050 24.034 0.000 1.105 1.301
## i 1.309 0.148 8.864 0.000 1.019 1.598
## f1RRses 0.749 0.082 9.174 0.000 0.589 0.909
## f1cupermitc 0.632 0.094 6.712 0.000 0.447 0.816
## f1custructurec 0.385 0.042 9.270 0.000 0.303 0.466
## scl1dep 0.370 0.074 5.036 0.000 0.226 0.514
## scl1depent 0.068 0.005 12.872 0.000 0.058 0.079
## f1mextprbc 134.345 10.499 12.796 0.000 113.767 154.923
## Std.lv Std.all
## 0.044 0.667
## 0.249 1.000
## 1.203 1.000
## 1.309 1.000
## 0.749 1.000
## 0.632 1.000
## 0.385 1.000
## 0.370 1.000
## 0.068 1.000
## 134.345 1.000
##
## R-Square:
## Estimate
## s 0.333
##Creating graphs
####
library(modelr)
library(ggplot2)
bestcov <- lm(
s ~ f1csexc + f1cagec + i + f1RRses + f1cupermitc + f1custructurec + scl1dep + f1mextprbc,
data = dat
)
dat <-
dat %>%
add_residuals(bestcov, var = "resid_s")
dat %>%
ggplot(aes(scl1depent, resid_s)) +
geom_point(size = 4, alpha = 1/2, colour = "blue") +
geom_smooth(method = "lm", size = 2, color = "darkblue") + # +
ylim(-0.5,0.5) +
#xlim(-0.5,0.5) +
labs(
x = "Depression entropy",
y = "VF Slope (residuals)"
) + theme(text = element_text(size=15))
## `geom_smooth()` using formula 'y ~ x'
